The deep mining of consumer behaviour data on product network marketing platform Online publication date: Fri, 05-Apr-2024
by Chunming Yu; Xin Jin
International Journal of Product Development (IJPD), Vol. 28, No. 1/2, 2024
Abstract: To overcome the problems of low accuracy of behaviour analysis results and low purchase proportion in traditional methods, a deep mining method of consumer behaviour data on product network marketing platform is proposed. Firstly, according to the nearest neighbour data distribution, the relevant subspace of consumer behaviour data of product e-marketing platform is divided, and the sparsity difference of relevant subspace data in each attribute is calculated. Then, the filtering of outlier interference data is completed by setting the difference threshold of local sparsity factor. Finally, the multi-source data mining method is used to analyse the difference between the data attribute weight and the target weight of each attribute behaviour, so as to realise the in-depth mining of consumer behaviour data. Test results show that the maximum error of consumer behaviour analysis of the design method is only 1, and the purchase proportion after secondary marketing reaches 10.2%.
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